File size: 5,947 Bytes
b49fff1 3cf3d90 e5339a7 debc0e4 834674b e8415bd b49fff1 dcfa973 5961ad0 b49fff1 63f8eb0 7d5ed03 9162bbc 5961ad0 74515fe 5961ad0 1e8533b 5961ad0 a320b12 5961ad0 dcfa973 1e8533b 5961ad0 1e8533b a320b12 5961ad0 dcfa973 5961ad0 fd31936 8fafcef f82d214 8fafcef a49a4a5 8fafcef b4d6557 8fafcef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
---
library_name: transformers
tags: []
pipeline_tag: fill-mask
widget:
- text: "shop làm ăn như cái <mask>"
- text: "hag từ Quảng <mask> kực nét"
- text: "Set xinh quá, <mask> bèo nhèo"
- text: "ăn nói xà <mask>"
---
# 5CD-AI/visobert-14gb-corpus
## Overview
<!-- Provide a quick summary of what the model is/does. -->
We continually pretrain `uitnlp/visobert` on a merged 14GB dataset, the training dataset includes:
- Crawled data (100M comments and 15M posts on Facebook)
- UIT data, which is used to pretrain `uitnlp/visobert`
- MC4 ecommerce
Here are the results on 4 downstream tasks on Vietnamese social media texts, including Emotion Recognition(UIT-VSMEC), Hate Speech Detection(UIT-HSD), Spam Reviews Detection(ViSpamReviews), Hate Speech Spans Detection(ViHOS):
<table>
<tr align="center">
<td rowspan=2><b>Model</td>
<td rowspan=2><b>Avg MF1</td>
<td colspan=3><b>Emotion Recognition</td>
<td colspan=3><b>Hate Speech Detection</td>
<td colspan=3><b>Spam Reviews Detection</td>
<td colspan=3><b>Hate Speech Spans Detection</td>
</tr>
<tr align="center">
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
</tr>
<tr align="center">
<td align="left">viBERT</td>
<td>78.16</td>
<td>61.91</td>
<td>61.98</td>
<td>59.7</td>
<td>85.34</td>
<td>85.01</td>
<td>62.07</td>
<td>89.93</td>
<td>89.79</td>
<td>76.8</td>
<td>90.42</td>
<td>90.45</td>
<td>84.55</td>
</tr>
<tr align="center">
<td align="left">vELECTRA</td>
<td>79.23</td>
<td>64.79</td>
<td>64.71</td>
<td>61.95</td>
<td>86.96</td>
<td>86.37</td>
<td>63.95</td>
<td>89.83</td>
<td>89.68</td>
<td>76.23</td>
<td>90.59</td>
<td>90.58</td>
<td>85.12</td>
</tr>
<tr align="center">
<td align="left">PhoBERT-Base </td>
<td>79.3</td>
<td>63.49</td>
<td>63.36</td>
<td>61.41</td>
<td>87.12</td>
<td>86.81</td>
<td>65.01</td>
<td>89.83</td>
<td>89.75</td>
<td>76.18</td>
<td>91.32</td>
<td>91.38</td>
<td>85.92</td>
</tr>
<tr align="center">
<td align="left">PhoBERT-Large</td>
<td>79.82</td>
<td>64.71</td>
<td>64.66</td>
<td>62.55</td>
<td>87.32</td>
<td>86.98</td>
<td>65.14</td>
<td>90.12</td>
<td>90.03</td>
<td>76.88</td>
<td>91.44</td>
<td>91.46</td>
<td>86.56</td>
</tr>
<tr align="center">
<td align="left">ViSoBERT</td>
<td>81.58</td>
<td>68.1</td>
<td>68.37</td>
<td>65.88</td>
<td>88.51</td>
<td>88.31</td>
<td>68.77</td>
<td>90.99</td>
<td><b>90.92</td>
<td><b>79.06</td>
<td>91.62</td>
<td>91.57</td>
<td>86.8</td>
</tr>
<tr align="center">
<td align="left">visobert-14gb-corpus</td>
<td><b>82.2</td>
<td><b>68.69</td>
<td><b>68.75</td>
<td><b>66.03</td>
<td><b>88.79</td>
<td><b>88.6</td>
<td><b>69.57</td>
<td><b>91.02</td>
<td>90.88</td>
<td>77.13</td>
<td><b>93.69</td>
<td><b>93.63</td>
<td><b>89.66</td>
</tr>
</div>
</table>
## Usage (HuggingFace Transformers)
Install `transformers` package:
pip install transformers
Then you can use this model for fill-mask task like this:
```python
from transformers import pipeline
model_path = "5CD-AI/visobert-14gb-corpus"
mask_filler = pipeline("fill-mask", model_path)
mask_filler("shop làm ăn như cái <mask>", top_k=10)
```
## Fine-tune Configuration
We fine-tune `5CD-AI/visobert-14gb-corpus` on 4 downstream tasks with `transformers` library with the following configuration:
- seed: 42
- gradient_accumulation_steps: 1
- weight_decay: 0.01
- optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
- training_epochs: 30
- model_max_length: 128
- learning_rate: 1e-5
- metric_for_best_model: wf1
- strategy: epoch
And different additional configurations for each task:
| Emotion Recognition | Hate Speech Detection | Spam Reviews Detection | Hate Speech Spans Detection |
| --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
|\- train_batch_size: 64<br>\- lr_scheduler_type: linear | \- train_batch_size: 32<br>\- lr_scheduler_type: linear | \- train_batch_size: 32<br>\- lr_scheduler_type: cosine | \- train_batch_size: 32<br>\- lr_scheduler_type: cosine |
|